4 accepted papers
Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pressing need for automation. The primary obsta- cle lies in the severe lack of data—there is much fewe…
Language models of code (LMs) work well when the surrounding code provides sufficient context. This is not true when it becomes necessary to use types, functionality or APIs defined elsewhere in the repository or a linked library, especially those not seen during training. LMs suffer from limited aw…
Synthesizing inductive loop invariants is fundamental to automating program verification. In this work we observe that Large Language Models (such as {gpt-3.5} or {gpt-4}) are capable of synthesizing loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate…
Large language models (LLMs) have demonstrated an impressive ability to generate code for various programming tasks. In many instances, LLMs can generate a correct program for a task when given numerous trials. Consequently, a recent trend is to do large scale sampling of programs using a model and…